Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Cryptocurrency Price Prediction Study Using Deep Learning and Machine Learning Subject The topic of the resource Bitcoin; Cryptocurrency; Ethereum; Long short-term memory; Price prediction; Random forest; Time series Description An account of the resource A cryptocurrency is a network-based computerized exchange that makes imitation and double-spending pretty much impossible. Many cryptocurrencies are built on distributed networks based on blockchain technology, which is a distributed ledger enforced by a network of computers. Thanks to blockchain technology, transactions are secure, transparent, traceable, and immutable. As a result of these traits, cryptocurrency has increased in popularity, especially in the financial industry. This research looks at a few of the most popular and successful deep learning algorithms for predicting bitcoin prices. LSTM and Random Forest outperform our generalized regression neural architecture benchmarking system in terms of prediction. Bitcoin and Ethereum are the only cryptocurrencies supported. The approach can be used to calculate the value of a number of different cryptocurrencies. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Siddharth D.; Kaushik J. Source A related resource from which the described resource is derived Cognitive Science and Technology, pp. 669-677. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-19-8086-2_64" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-19-8086-2_64</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149993794&amp;doi=10.1007%2F978-981-19-8086-2_64&amp;partnerID=40&amp;md5=7ce9a8a08383177eaf78446e83edd875" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149993794&amp;doi=10.1007%2f978-981-19-8086-2_64&amp;partnerID=40&amp;md5=7ce9a8a08383177eaf78446e83edd875</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 21953988 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Siddharth D., Department of Data Science, CHRIST (Deemed to be University), Bengaluru, India; Kaushik J., Department of Data Science, CHRIST (Deemed to be University), Bengaluru, India, MIT-ADT University, Maharashtra, Pune, India